Optimal Multiuser Diversity in Multi-Cell MIMO Uplink Networks: User Scaling Law and Beamforming Design

Abstract

Abstract:We introduce a distributed protocol to achieve multiuser diversity in a multicell multiple-input multiple-output (MIMO) uplink network, referred to as a MIMO interfering multiple-access channel (IMAC). Assuming both no information exchange among base stations (BS) and local channel state information at the transmitters for the MIMO IMAC, we propose a joint beamforming and user scheduling protocol, and then show that the proposed protocol can achieve the optimal multiuser diversity gain, i.e., KM log(SNR log N), as long as the number of mobile stations (MSs) in a cell, N, scales faster than SNR KM−L 1− for a small constant > 0, where M, L, K, and SNR denote the number of receive antennas at each BS, the number of transmit antennas at each MS, the number of cells, and the signal-to-noise ratio, respectively. Our result indicates that multiuser diversity can be achieved in the presence of intra-cell and inter-cell interference even in a distributed fashion. As a result, vital information on how to design distributed algorithms in interference-limited cellular environments is provided.

Opportunistic Interference Alignment for Spectrum Sharing between Radar and Communication Systems

“…(Note that the radars are assumed to be located near the communication system in our system model and thus it is possible that the received signal-to-noise ratio (SNR) of the desired radar signal at radar systems has similar levels with interference-to-noise ratio (INR) of the interference signal from UEs of communication systems). We assume local channel station information (CSI) (i.e., CSI between itself and others) are available at all transmitting nodes in the system by the reference signals that is broadcasted from all receiving nodes as in [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].…”

“…The basic concept of IA is to confine interference from other users into a pre-defined linear space at the receiver at the user of interest and to separate the desired signal space from the interference space. In addition, an opportunistic interference alignment (OIA) technology has been proposed for effectively combining the IA technique with user scheduling technique for both multi-user downlink and uplink cellular networks [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. The OIA technique opportunistically selects the users amongst all users in each cell in the sense that inter-cell interference (ICI) is aligned at a pre-defined interference space.…”

A Novel Codeword Selection Scheme for MIMO-MAC Lower-Bound Maximization

“…Then it finds the location index Limited feedback [ 25 , 26 ]. Because the channel matrix dimension is larger than the precoding matrix, the feedback precoding algorithm is better, and the limited feedback of IA achieves greater performance improvement with less feedback and has been widely studied [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

“…The limited feedback of IA shares the same codebook between the transmitter and receiver, and the receiver quantizes the channel matrix or precoding according to the obtained CSI and sends feedback for the location index of the quantization codeword [ 3 , 4 ]. Because the quantized channel matrix has a larger dimension than the quantized precoding matrix, the quantized precoding scheme has been widely studied [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. This paper proposes a MIMO Multiple Access Channel (MIMO-MAC) limited feedback IA algorithm that maximizes the rate lower-bound of the system user.…”

Revisiting Degrees of Freedom of Full-Duplex Systems with Opportunistic Transmission: An Improved User Scaling Law

“…In addition, such opportunism was utilized in multi-cell broadcast channels (or, equivalently, interfering broadcast channels) by using multi-cell random beamforming [ 16 , 17 ] and opportunistic interference alignment [ 18 ]. As a more challenging problem than the downlink case, the optimal DoF in multi-cell multiple access channels (or, equivalently, interfering multiple access channels) was analyzed by presenting opportunistic interference alignment strategies [ 19 , 20 , 21 , 22 ] and distributed scheduling protocols [ 23 , 24 ]. In [ 16 , 18 , 19 , 20 , 21 ], the minimum number of users required to achieve the optimal DoF was investigated (i.e., the user scaling law).…”